Trust Impacts Driver Glance Strategy in Multitasking

Abstract

Growing evidence supports the idea that patterns of gaze are important to human-machine trust, as they are to human-to-human trust (LaFrance & Mayo, 1976; Kendon, 1967), and indeed potentially all primate social dynamics (Emery, 2000). A growing literature explores trust and gaze toward anthropomorphic robots (Mutlu et al., 2009; Stanton & Stevens, 2014; Van de Brule et al., 2014, Hancock et al., 2011). Less work has investigated far-more-common non-anthropomorphic systems, despite evidence suggesting that operators deploy the same trust patterns toward such interface that they might toward fellow humans (Nass, 1996; Fogg & Nass, 1997), and that they change patterns of visual allocation based upon that trust (Hergeth et al., 2016; Geitner at al., 2017). In critical operational settings, such as driving while multitasking, maximum safety and stability is associated with maximum visual attention devoted to the road (Hancock & Warm, 1989; Strayer, Drews & Johnston, 2003; Sawyer et al., 2014). Social gaze strategies deployed toward an interface suggest competition for these resources, and so applied consequences in terms of adopting appropriate information gathering strategies.

Individual Differences in Glance Patterns under Distraction in Level 2 Automated Driving

“…Although the data in this study did not reveal the factors that contributed to different glance patterns, previous studies have shed light on this topic by showing the relationship between driver trust in automation and their glance behavior (Hergeth, Lorenz, Vilimek, & Krems, 2016;Sawyer, Seppalt, Mehler, & Reimer, 2017). Higher trust in automation, lower frequency of monitoring on automation during distraction (Hergeth et al, 2016).…”

Toward a Theory of Visual Information Acquisition in Driving

“…There is some hope for broadly classifying task and behavior based on eye movements, although the classifications may be less precise than desired for characterizing information acquisition. We know that expert and novice drivers have very different scanpaths, both in hazard detection (Crundall, 2016) and more broadly (Mourant & Rockwell, 1972), while age is also a factor in how drivers use eye movements to acquire specific information (Sawyer et al, 2016) while gaze behavior is also influenced by the degree to which the driver trusts the vehicle (Sawyer, Seppalt et al, 2017) and their level of fatigue (Ji et al, 2004). For that matter, the features of the environment itself seem to have a profound impact on drivers’ ability to predict immediate events (Wolfe, Fridman et al, 2019), which implies that gazepaths in different environments, for example, highway versus urban, are likely to be discriminable.…”